Funnels, flows, retention, sessionization--nearly all ‘Product Analytics Manifestos’ tout these methods and KPI tracking techniques as key data-driven product management practices, but very few actually give an outline of what your workflow should look like. What questions should I start with? What should my final result look like? What actions should I take as a result?
It's no longer enough to only see funnel conversion and dropoff. While that is of course important as a starting point, it only paints a small picture and doesn’t actually tell you why people are either dropping off, converting, or retained. None of these methods for understanding user experience or the importance of certain features exist in a vacuum, but many tools make it difficult to use the result of one type of analysis inside the subsequent analysis of another.
Product Managers should be able to ask questions like:
- How does the time between two steps in a funnel affect the overall conversion percentage?
- What is the effect of funnel conversion on retention?
- How many sessions with 5+ page views in a user’s first week does a user need to have to be retained in week 10?
- For users that converted through a 4 step funnel within 7 days, what sequence of onboarding events did they experience on their first day?
Without a team of analysts and data scientists or the right tools for the job, these questions can seem like pipe dreams. However, technology has evolved to the point today where Product Managers can start with one analysis, and without writing any code, evolve their queries into extremely actionable product-driving insights.
Not sure where to start to make this possible?
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1. Think about funnels you already know about your product or platform.
- What are some of the “key” things that users do or perform? For example, users in a music app like Spotify might progress through a “Share Playlist” funnel by playing songs, creating a playlist, and finally sharing it. If you have paid users, that Subscription event would definitely be a “key” event.
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- 2. Build your funnel and see where drop-off can be improved.
- Is there one step in your funnel that has a much higher drop-off than expected?
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- 3. Identify common sequences.
- For the users that dropped off, what were the sequences of events that were taken from the previously converted milestone? What about the users that didn’t drop off?
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- 4. Compare the two results.
- Are there certain paths converters take that non-converters do not?
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- 5. Validate the importance of your analysis.
- Take a look at how the number of funnel conversions or “key” events affects overall retention. Is there a magic number of funnel conversions by the end of a user’s 1st, 2nd, or 4th week that users should experience in order to be retained by week 10 or 12?
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- 6. Rinse and repeat!
- Using this method, what is the ideal first-week experience that users should have in order to be retained?
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As we’ve seen from the progression of analyses, the results of one query affect how we think about what should come next, and we must be able to evolve our queries to get to actionable insights and ultimately be sure we’re making the right product decisions.